Fracture detection from water saturation log data using a Fourier-wavelet approach

Fracture detection as applied to reservoir characterization is a key step towards modeling of fracturedreservoirs. While different methods have been proposed for detection and characterization of fractures and fractured zones, each is associated with certain shortcomings that prevent from their full use in different related engineering application environments. In this paper a new method is proposed for detection of fractured zones and fracture density in which water saturation log data is utilized. For detection of fractures, we have used wavelet transform and properties of wavelets that are highly suitable for detection of changes and local features of data. To choose the optimum mother wavelet, we have used energy matching strategy in which a wavelet with the highest energy match between spectral energy of the signal at the dominant frequency band and the coefficient energy at the same band of wavelet decomposition of the signal is selected. We have used wavelet packet for a more narrow frequency band selection and enhanced results. Decomposing the water saturation data using wavelets showed that the majority of information of theoriginal log is hidden at low frequency bands. As a result, approximated section of wavelet transform of data was used for fracture detection, while shale volume (or gamma ray) log data was used to filter part of the errors in prediction and identification of the uncertain zones. This increased the accuracy of the results by 70%. Finally, a linear relation was derived between energy of approximated section of water saturation log and fracture density, allowing us to estimate the number of fractures in each fractured zone. The method was applied to four wells belonging to one of the Iranian oilfields located in the southwest region of the country and the results are promising. The use of large volume of data and the subsequent analysis increased the generalization ability of the proposed method

Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques

Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human–Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representations, and its applications include smart homes and buildings, action prediction, crowd counting, patient rehabilitation, and elderly monitoring. Traditionally, HAR has been performed through vision-based, sensor-based, or radar-based approaches. However, vision-based and sensor-based methods can be intrusive and raise privacy concerns, while radar-based methods require special hardware, making them more expensive. WiFi-based HAR is a cost-effective alternative, where WiFi access points serve as transmitters and users’ smartphones serve as receivers. The HAR in this method is mainly performed using two wireless-channel metrics: Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). CSI provides more stable and comprehensive information about the channel compared to RSSI. In this research, we used a convolutional neural network (CNN) as a classifier and applied edge-detection techniques as a preprocessing phase to improve the quality of activity detection. We used CSI data converted into RGB images and tested our methodology on three available CSI datasets. The results showed that the proposed method achieved better accuracy and faster training times than the simple RGB-represented data. In order to justify the effectiveness of our approach, we repeated the experiment by applying raw CSI data to long short-term memory (LSTM) and Bidirectional LSTM classifiers

Enhancing Industry Exposure, Discovery-Based and Cooperative Learning in Mechanics of Solids

BACKGROUND Mechanics of Solids is a second year undergraduate subject, undertaken by both Civil and Mechanical engineering students at the University of Technology, Sydney (UTS). Mechanics of Solids has been delivered for many years in a traditional format with lectures and problem solving tutorials. As part of a national Australian project “Enhancing Industry Exposure in Engineering Degrees”, UTS in partnership with other universities and industry partners in Australia has sought industry involvement to engage students with the real-world challenges of engineering practice. PURPOSE The main objective of this project is to design, develop and implement learning modules in Mechanis of Solids that integrate industry exposure to provide context for the concepts included in this subject. DESIGN The project consisted of six guest lectures by industry representatives on topics related to typical Mechanics of Solids subject matter and two seminars on using MDSolids software. Students completed a collaborative assignment aligned with one of the industry presentations. Their reports and presentations were assessed on assessment criteria which included contextual understanding, judgement, effective collaboration and creativity, and their perceptions were captured to evaluate the impact of industry engagement in this subject. RESULTS One of the major benefits of this project was students’ better understanding of engineering practice. There were also positive effects on students’ motivation for learning engineering. CONCLUSIONS This paper reports the major findings, outcomes and challenges for implementing enhancing industry exposure approach in Mechanics of Solids subject at UTS. The main finding of this research concluded that this project is very valuable to both students as it promotes exposure to real-world engineering challenges. The students’ exposure to real and substantive challenges improves their contextual understanding, plus their judgement, practice based planning, teamwork, and initiative learning skills

Ten Simple Rules for Reproducible Research in Jupyter Notebooks

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses

On the presence of humpback whales in the Persian Gulf: rare or rarely documented? Report of the IWC Scientific Committee Meeting SC/67A/CMP/14, Bled, Slovenia, May 2017

We critically review the evidence for humpback whale presence in the Persian Gulf. Five specimen records, assumed to belong to the endangered Arabian Sea population, are confirmed in the period 1883- 2017: Bassore Bay, Iraq; Doha, Qatar; Kuwait Inner harbour, Kuwait; Qeshm Island, Iran; and Akhtar, Bushehr Province, Iran. The two Iranian cases, both juveniles, are newly recorded. With accumulating reports, an alternate hypothesis to 'rare stragglers' deserves consideration, one in which Arabian Sea humpback whales may enter the Persian Gulf with some regularity, perhaps as normal visitors, if not permanent residents. Deficiency of records may reflect a general sparsity of whale research effort in the Persian Gulf. The historical description of Megaptera indica Gervais, 1883 is translated from French

CLONE-BASED MOBILE AGENT ITINERARY PLANNING USING SEPARATE TREES FOR DATA FUSION IN WSNS

ABSTRAC

A fluorescence-based reporter substrate for monitoring RNA editing in trypanosomatid pathogens

RNA editing regulates mitochondrial gene expression in trypanosomatid pathogens by creating functional mRNAs. It is catalyzed by a multi-protein complex (the editosome), and is found to be essential in both insect stage and mammalian blood stream form of Trypanosoma brucei. This particular form of RNA editing is unique to trypanosomatids, and thus provides a suitable drug target in trypanosomatid pathogens. Here, we demonstrate the feasibility of a rapid and sensitive fluorescence-based reporter assay to monitor RNA editing based on ribozyme activity. We could validate our new assay using previously identified inhibitors against the essential RNA editing ligase. The principle advantages of this assay are: (i) the use of non-radioactively labeled materials, (ii) sensitivity afforded by fluorescence instrumentation applicable to high-throughput screening of chemical inhibitors against the essential editosome and (iii) a rapid and convenient ‘mix and measure’ type of assay in low volume with a high signal to noise ratio. This assay should enhance rapid identification and characterization of the editosome inhibitors primarily based on the overall composition of the editosomes from T. brucei. These inhibitors could also be tested against the editosomes from the closely related pathogens including T. cruzi and Leishmania species

Biallelic UBE4A loss-of-function variants cause intellectual disability and global developmental delay

Purpose: To identify novel genes associated with intellectual disability (ID) in four unrelated families. Methods: Here, through exome sequencing and international collaboration, we report eight individuals from four unrelated families of diverse geographic origin with biallelic loss-of-function variants in UBE4A. Results: Eight evaluated individuals presented with syndromic intellectual disability and global developmental delay. Other clinical features included hypotonia, short stature, seizures, and behavior disorder. Characteristic features were appreciated in some individuals but not all; in some cases, features became more apparent with age. We demonstrated that UBE4A loss-of-function variants reduced RNA expression and protein levels in clinical samples. Mice generated to mimic patient-specific Ube4a loss-of-function variant exhibited muscular and neurological/behavioral abnormalities, some of which are suggestive of the clinical abnormalities seen in the affected individuals. Conclusion: These data indicate that biallelic loss-of-function variants in UBE4A cause a novel intellectual disability syndrome, suggesting that UBE4A enzyme activity is required for normal development and neurological function

Correction to: "Comparative repair capacity of knee osteochondral defects using regenerated silk fiber scaffolds and fibrin glue with/without autologous chondrocyes during 36 weeks in rabbit model (Cell and Tissue Research, (2016), 364, 3, (559-572), 10.1007/s00441-015-2355-9)

In this paper, figure 1 and its associated text were erroneously identical to that of another article from our group (Mobini et al., 2016, Journal of Biomaterial Application, SAGE publications). Unfortunately, copyright permission to re-use figure 1 and its related data were not requested. The authors would like to apologize for any confusion caused in this regard. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature